1. Field of the Invention
The present invention relates to image recognizing apparatus and method which are suitably used to discriminate an object by extracting a feature quantity.
2. Description of the Related Art
Conventionally, a technique of discriminating whether or not a subject in an image is identical with a subject in another image has been known. Particularly, a face discriminating technique of discriminating the face of an individual has been known. In the technique like this, as a factor of deteriorating discrimination performance, there is a variation which occurs between a registration pattern and an authentication pattern. More specifically, variations which occur due to illumination condition, direction/pose, hiding by another object, expression and the like become the factor of deteriorating discrimination performance.
To prevent such deterioration of the discrimination performance, there is a method of paying attention to a partial portion of an object in the image. For example, when the object is a person's face, influence of such variations as above does not appear uniformly in the whole area of the face. Namely, in the case where the variation occurs due to the expression, if it is assumed that the image of a face showing expression and the image of a face showing no expression are compared, then it is thought, between these images, that the variation near the nose is smaller than that of the mouth or the eyes. Moreover, in the case where the variation occurs due to the illumination, if strong illumination light is obliquely struck, it is though that the magnitude of the variation in the portion where the light is struck is difference from the magnitude of the variation in the portion where the oblique light is not struck. Moreover, if it is assumed that, in the image, the face direction is pointed to the left relatively from the observer's side, since the left side of the face is the rear side and thus hidden because of the three-dimensional shape of the face, it is thought that the variation between the front face and the right-side face is larger than that between the front face and the left-side face.
As just described, if the variations of the expression, the illumination, the face direction and the like occur, there is a possibility that, even if the variation in a local area is extremely high, the variation in another local area appears to the extent that an individual can be identified. Consequently, if similarities of the respective local areas in which the variations are comparatively small are selectively integrated and used, it becomes possible to discriminate the individual with a high degree of accuracy.
Incidentally, to cope with a large variation, it is thought that registration patterns respectively corresponding to a plurality of variation patterns are previously registered. For example, in the case of person's face, it is thought that images respectively corresponding to the variations of the illumination condition, the direction/pose, the hiding, the expression and the like are previously registered for each person intended to be registered. That is, by previously holding, as the registration image, the image corresponding to the condition which is likely to occur when photographing, it is possible to improve recognition accuracy. However, previous preparation of a large number of registration images contradicts user-friendliness. Moreover, it is actually difficult to predict all the variation patterns and prepare the registration images corresponding to the predicted patterns.
In consideration of such inconvenience, Japanese Patent Application Laid-Open No. 2011-086265 adopts the method which can cope with a large variation with few patterns while paying attention to local portions. In this method, the pattern is divided into partial areas, and predetermined conversion is performed to the partial feature extracted from each partial area, thereby calculating the feature quantity which is robust to the variation. Here, it should be noted that the predetermined conversion can cope with the variations adaptively by switching parameters according to attributes such as the direction, the expression and the like of a person's face.
Moreover, in J. Wright and G. Hua “Implicit elastic matching with random projections for pose-variant face recognition”, In Proc, CVPR, 2009, face recognition which is robust to a variation of pose is performed using a histogram of features quantized by random projection.
To calculate the variation-robust feature quantity by performing the conversion to the partial feature extracted from the local portion, it is necessary to hold a large number of parameters to be used for the conversion. For example, in case of performing the conversion using a linear operation as typified by main component analysis, if it is assumed that a previous feature dimension is N and a post-conversion feature dimension is M, then a parameter quantity is in proportion to N×M. In case of using the method which pays attention to the local portion, parameters as many as the number of the local portions are further necessary. Thus, the storage area increases to secure the parameters as data. Besides, the memory band for reading the parameter to the memory area when performing the conversion operation increases, so that the power consumption increases. As a result, various problems occur.
However, in the above non-patent literature, any problem of parameter or process quantity is not at all considered. Meanwhile, in the method described in Japanese Patent Application Laid-Open No. 2011-086265, the conversion which is normally performed in two steps is performed in one step, thereby reducing the parameter quantity. However, even in this method, since the parameter quantity increases in proportion to the product of the number of dimensions of the feature quantity and the number of dimensions of the post-conversion feature quantity, and the number of the local portions as described above, essential improvement is not performed yet.
The present invention aims to reduce the holding quantity of the parameters for reducing the number of dimensions of the feature quantity extracted from the image and reduce the number of dimensions of the feature quantity by using the high-accuracy parameter.